ASTRA-sim 3.0: Next-Level Distributed Machine Learning Simulations via High-Fidelity GPU and Infrastructure Modeling
William Won, Jinsun Yoo, Tuan Ta, Moumita Dey, Andy Balogh, Pradosh Datta, Furkan Eris, Conor Green, Winston Liu, Changhai Man, Kingshuk Mandal, Amos Rai, Vinay Ramakrishnaiah, Ruchi Shah, David Sidler, Harsh Sikhwal, Hanjiang Wu, Tushar Krishna, Bradford M. Beckmann
Abstract
Distributed machine learning (ML) is a key paradigm for today's large-scale artificial intelligence applications. As model inference arises as an important use case, faithful modeling of latency-sensitive collective communication has never been more important. Capturing the device architecture and modeling control and data paths at high fidelity is therefore a necessity today. Having a common, detailed representation for distributed ML infrastructure is also crucial. We revisit the promising open-source, community-driven simulator: ASTRA-sim. In this work, we identify limitations of the current ASTRA-sim simulator and augment it with new features. To this end, we enable fine-grained, high-fidelity simulation with a standardized infrastructure representation, opening new design space exploration opportunities. We propose the simulation at cache-line-sized load-store granularity, with a detailed graphics processing unit (GPU) execution model, to balance simulation scalability and fidelity. We also introduce InfraGraph, a standardized representation to capture distributed ML network infrastructure in detail. Using the updated ASTRA-sim 3.0 simulator, we showcase interesting design space explorations for designing optimized collective algorithms, network requirements, and GPU architectures.
Topics
Classified with taxonomy v2 on Wed, 2 Sept 2026.